Author
Ehsan Nazemi
Other affiliations: Duy Tan University, Islamic Azad University, Shahid Beheshti University
Bio: Ehsan Nazemi is an academic researcher from University of Antwerp. The author has contributed to research in topics: Volume (thermodynamics) & Detector. The author has an hindex of 23, co-authored 61 publications receiving 1280 citations. Previous affiliations of Ehsan Nazemi include Duy Tan University & Islamic Azad University.
Papers
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TL;DR: In this article, a method based on dual modality densitometry using artificial neural network (ANN) was presented to first identify the flow regime and then predict the void fraction in two-phase flows.
137 citations
TL;DR: In this paper, a multilayer perceptron neural network was used to predict void fraction in gas-eliquid two-phase flows with a mean relative error of < 1.4%.
131 citations
TL;DR: In this article, the group method of data handling (GMDH) technique was applied in order to increase measuring precision of a simple photon attenuation based two-phase flowmeter that has the ability to estimate the gas volumetric percentage in a two phase flow without any dependency to flow regime pattern.
122 citations
TL;DR: In this paper, a gamma-ray transmission technique is used to measure the void fraction and identify the flow regime of a two-phase flow using two detectors which were optimized in terms of detector orientation.
122 citations
TL;DR: A proposed ANN architecture is used to predict the oil, water and air percentage, precisely, based on nuclear technique in annular multiphase regime using only one detector and a dual energy gamma-ray source.
121 citations
Cited by
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TL;DR: Examination of several Machine Learning models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms are examined.
241 citations
TL;DR: In this paper, the authors reviewed the applications of ANN for thermal analysis of heat exchangers and highlighted the limitations of ANN in this field and its further research needs in the field.
232 citations
01 Jan 1984
173 citations
TL;DR: In this article, a method based on dual modality densitometry using artificial neural network (ANN) was presented to first identify the flow regime and then predict the void fraction in two-phase flows.
137 citations